Novel treatment strategies in metastatic colorectal cancer patients with KRAS wildtype tumors
Dataset #6 normalized WP-031 · plasma samples of patients with colorectal cancer, postoperative...
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less abundant peaks that gave lower Mascot scores were found to be differentially expressed between CRC, IBD and healthy subjects. 5. Discussion The work presents a MALDI-nanochip based protein profiling and identification workflow for the analysis of exosomal proteins as potential clinical biomarkers. Using bioinformatics, data analysis of mass spectral features, samples from patients with colon cancer, IBD and healthy subjects could be clearly separated. The peak intensities were found to vary greatly depending on the method of sample cleanup. By using the Tethis slide we were able to remove some of the more abundant proteins which are usually detected using ZipTip cleanup and discover otherwise undetected discriminant peaks. This looks promising to establish a high- throughput screening platform for clinical purposes (e.g. cancer diagnostics) in the future. We now aim to develop this method further to support patient group differentiation based on putative marker peaks with confident protein identifications. 6. References 1.Serafim, V. et al. Classification of cancer cell lines using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and statistical analysis. Int J Mol Med, 40, 1096-1104 (2017). 2.Stübiger G. et al. MALDI-MS Protein Profiling of Chemoresistance in Extracellular Vesicles of Cancer Cells. Anal Chem, 90, 13178-13182 (2018). 1. Overview We previously demonstrated we could rapidly distinguish fluorouracil resistant cancer sample groups based on protein profiling of extracellular vesicles using a linear benchtop MALDI TOF instrument [1]. The aim of this follow up work is to identify the proteins that are differentially expressed in the different sample groups in order to better understand the disease processes and to support the rapid screening approach developed previously. Here we present the results from this study using a high performance reflectron MS/MS MALDI-TOF platform (Fig. 1) for the comparative proteomic profiling of circulating extracellular vesicles (EV) extracted from plasma samples of patients with colorectal cancer, postoperative colorectal cancer patients, IBD patients and a healthy control group in view of liquid biopsy applications (as a potential application for liquid biopsy oncological diagnosis). 2. Introduction Exosomes are small cell-derived vesicles (50-150 nm) which are increasingly recognised as a promising source of circulating biomarkers for non-invasive diagnostics from body fluids (liquid biopsy). MALDI-MS profiling of exosomal proteins was demonstrated as being capable to detect cancer-cell specific molecular signatures which can be used to differentiate between cancer types and stages as well as different grades of chemoresistance of cancer cells [1, 2]. This distinguishes MALDI-MS as promising tool for application in liquid biopsy based cancer diagnostics. However, the identification of the disease-related exosomal biomarkers represents a challenging task. Here we present a MALDI-nanochip platform in combination with bioinformatics data analysis for the comparative profiling and detection of exosomal proteins as potential cancer biomarkers. 3. Methods Blood samples were prepared according to standard methods and exosomes were isolated using sequential (ultra)centrifugation. Proteins were solvent-extracted, dried under vacuum and stored at -80°C before analysis. Proteins were directly analysed and subsequently subjected to tryptic digestion after application to the MALDI-nanochips (Tethis) (Fig. 1). On-chip digests were dried, washed and covered with 0.5 μL CHCA in ACN:2.5%TFA = 70:30 (v/v). Alternatively, samples were digested in-solution, desalted using C18-ZipTips and applied to FlexiMass-DS targets (Shimadzu). For protein profiling the AXIMA-Performance (Shimadzu) instrument was used. Protein identification was performed using the MALDI-7090 MALDI-TOF/TOF mass spectrometer (Shimadzu) with Mascot protein database search (Swiss/Uniprot). Statistical analysis was performed using Clover MS Data Analysis (Clover Biosoft) and eMSTAT (Shimadzu) software. 4. Results 40 plasma samples from patients with colon cancer (CRC pre/post operative), inflammatory bowel disease (IBD) and healthy controls were used for evaluation. First, protein extracts were analysed by MALDI-MS in the range of m/z 2000-20000 which has previously been shown to contain most informative peaks of exosomes [2]. A comparison of the mass spectra showed distinct differences particularly in the range above m/z 8000 after exosome isolation. PLS-DA of the whole dataset recorded on the MALDI-nanochip showed a good clustering and separation of the samples belonging to the four study groups (Fig. 2). Next, tryptic digests of the individual samples were subjected to peptide mass fingerprinting (PMF) in order to identify the discriminatory peptide peaks between the study groups based on multivariate data analysis (Fig. 3) and MS/MS analysis MALDI-nanochip based Screening of Exosomal Biomarkers: Application to Cancer Diagnostics Michael D. Nairn 1 ; Michael Wuczkowski 2 ; Jesús Jiménez 3 ; Iris Prinz 4 ; Marco Rissoglio 5 ; Emanuele Barborini 5,6 ; Gerald Stübiger 2, 7 1 Shimadzu, Manchester, United Kingdom; 2 Medical University of Vienna, Vienna, Austria; 3 Clover Bioanalytical Software, Granda, Spain; 4 Stratec Consumables, Salzburg, Austria; 5 Tethis, Milan, Italy; 6 Luxembourg Institute of Science and Technology; 7 Comprehensive Cancer Center, Vienna, Austria WP-031 Figure 1 – Overview of the sample preparation and analysis workflow. Centrifuge blood to isolate the plasma from the blood sample Spin at 300g to collect cells Spin at 10,000g and discard cell debris Spin at 100,000g to collect Extracellular vesicles Disclaimer: FOR RESEARCH USE ONLY. NOT FOR USE IN DIAGNOSTIC PROCEDURES. Collect blood sample from patient - Apply protein extract - Perform on-chip tryptic digest - wash with H2O Add matrix and acquire MS. Use eMSTAT to identify differentiation peaks. To determine identity of peaks, perform MSMS and submit results to Mascot Re-suspend the vesicles and extract proteins Sequential (ultra)centrifugation MALDI-nanochip processing MALDI-MS/MS analysis Blood collection and plasma preparation Figure 2 – Representative protein MALDI mass spectra of selected patient samples of the four study groups (A-D) recorded (i) before and (ii) after exosome isolation. PLS-DA plots of all patients of the study groups recorded using (iii) standard target slide and (iv) after MALDI-nanochip sample processing. Group A Group B Group C Group D Group A Group B Group C Group D 0 50 100 %Int. 4000 6000 8000 m/z 302 mV 304 mV 130 mV 291 mV Shimadzu Biotech Axima Performance 2.9.3.20110624 10000 15000 20000 m/z 4.4 mV 16 mV 1.3 mV 19 mV 0 50 100 0 50 100 0 50 100 #5A #11B #28C #38D 4000 6000 8000 m/z 96 mV 7.9 mV 200 mV 190 mV Shimadzu Biotech Axima Performance 2.9.3.20110624 6179.8 8754.4 10000 15000 20000 m/z 2.4 mV 0.3 mV 4.0 mV 2.0 mV 0 50 100 %Int. 0 50 100 0 50 100 0 50 100 #5A #11B #28C #38D (i) (ii) (iii) (iv) CRC pre CRC post IBD Controls Figure 3 – Comparison of MS spectra illustrating several peaks where the signal intensity has been enhanced when using Tethis slide sample washing (Red) versus Zip-Tip™ sample cleanup (Blue). was then performed on the discriminant peaks to identify the digested proteins using Mascot. From in-solution digests peaks of more abundant plasma proteins (e.g. alpha-1-antitrypsin, immunoglobulin heavy constant alpha 1, haptoglobin, fibrinogen gamma chain, etc.) were identified in selected samples of the study groups. These proteins were also found in the MALDI-nanochip processed samples but they showed no differentiation between the study groups. In contrast, several Figure 4 – Using a combination of statistical software (eMSTAT) and high resolution MSMS from HE-CID we are able to discover differences in protein expression in different patient populations. Differentially expressed proteins were then targeted for MSMS identification. This precursor was identified as IGHA1 - Immunoglobulin heavy constant alpha 1. 1578.7 S11 ZT LP50 PE2k MS_0001: J21 (Manual) S11 MTeth LP52 PE2k_0001: 4B1 (Manual) % Intensity 0 10 20 30 40 50 60 70 80 90 100 m/z 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Processed data (averaged) 69.4 mV 180.4 mV 1641.9 1836.0 1905.0 1683.0 2090.1 1550.8 1884.1 1820.0 2265.1 1671.9 1740.9 2045.1 1946.1 2102.2 2202.2 1488.8 2157.1 1888.0 1612.9 1994.1 1794.0 2283.1 1706.0 1953.1 2058.0 1561.8 1381.7 1471.8 2207.1 1337.8 2155.1 1431.8 1641.9 1835.9 1946.0 1808.0 1677.8 1874.0 1683.0 1743.9 1856.0 1794.0 1905.9 1872.0 1672.9 1629.8 1366.8 1740.9 1550.8 2057.9 1986.0 1882.0 2131.0 1669.7 1478.8 1816.9 1604.8 2005.0 2147.0 1386.7 2202.1 1951.0 2257.0 1535.7 2050.0 1470.8 1338.7 View publication stats View publication stats
Transcript of Dataset #6 normalized WP-031 · plasma samples of patients with colorectal cancer, postoperative...
less abundant peaks that gave lower Mascot scores were found to be differentially
expressed between CRC, IBD and healthy subjects.
5. Discussion
The work presents a MALDI-nanochip based protein profiling and identification
workflow for the analysis of exosomal proteins as potential clinical biomarkers.
Using bioinformatics, data analysis of mass spectral features, samples from
patients with colon cancer, IBD and healthy subjects could be clearly separated.
The peak intensities were found to vary greatly depending on the method of sample
cleanup. By using the Tethis slide we were able to remove some of the more
abundant proteins which are usually detected using ZipTip cleanup and discover
otherwise undetected discriminant peaks. This looks promising to establish a high-
throughput screening platform for clinical purposes (e.g. cancer diagnostics) in the
future. We now aim to develop this method further to support patient group
differentiation based on putative marker peaks with confident protein identifications.
6. References
1.Serafim, V. et al. Classification of cancer cell lines using matrix-assisted laserdesorption/ionization time-of-flight mass spectrometry and statistical analysis. Int J MolMed, 40, 1096-1104 (2017).2.Stübiger G. et al. MALDI-MS Protein Profiling of Chemoresistance in Extracellular Vesiclesof Cancer Cells. Anal Chem, 90, 13178-13182 (2018).
1. Overview
We previously demonstrated we could rapidly distinguish fluorouracil
resistant cancer sample groups based on protein profiling of extracellular
vesicles using a linear benchtop MALDI TOF instrument [1]. The aim of
this follow up work is to identify the proteins that are differentially
expressed in the different sample groups in order to better understand the
disease processes and to support the rapid screening approach developed
previously.
Here we present the results from this study using a high performance
reflectron MS/MS MALDI-TOF platform (Fig. 1) for the comparative
proteomic profiling of circulating extracellular vesicles (EV) extracted from
plasma samples of patients with colorectal cancer, postoperative colorectal
cancer patients, IBD patients and a healthy control group in view of liquid
biopsy applications (as a potential application for liquid biopsy oncological
diagnosis).
2. Introduction
Exosomes are small cell-derived vesicles (50-150 nm) which are
increasingly recognised as a promising source of circulating biomarkers for
non-invasive diagnostics from body fluids (liquid biopsy). MALDI-MS
profiling of exosomal proteins was demonstrated as being capable to
detect cancer-cell specific molecular signatures which can be used to
differentiate between cancer types and stages as well as different grades
of chemoresistance of cancer cells [1, 2]. This distinguishes MALDI-MS as
promising tool for application in liquid biopsy based cancer diagnostics.
However, the identification of the disease-related exosomal biomarkers
represents a challenging task. Here we present a MALDI-nanochip
platform in combination with bioinformatics data analysis for the
comparative profiling and detection of exosomal proteins as potential cancer
biomarkers.
3. Methods
Blood samples were prepared according to standard methods and exosomes were
isolated using sequential (ultra)centrifugation. Proteins were solvent-extracted,
dried under vacuum and stored at -80°C before analysis. Proteins were directly
analysed and subsequently subjected to tryptic digestion after application to the
MALDI-nanochips (Tethis) (Fig. 1). On-chip digests were dried, washed and
covered with 0.5 µL CHCA in ACN:2.5%TFA = 70:30 (v/v). Alternatively, samples
were digested in-solution, desalted using C18-ZipTips and applied to FlexiMass-DS
targets (Shimadzu). For protein profiling the AXIMA-Performance (Shimadzu)
instrument was used. Protein identification was performed using the MALDI-7090
MALDI-TOF/TOF mass spectrometer (Shimadzu) with Mascot protein database
search (Swiss/Uniprot). Statistical analysis was performed using Clover MS Data
Analysis (Clover Biosoft) and eMSTAT (Shimadzu) software.
4. Results
40 plasma samples from patients with colon cancer (CRC pre/post operative),
inflammatory bowel disease (IBD) and healthy controls were used for evaluation.
First, protein extracts were analysed by MALDI-MS in the range of m/z 2000-20000
which has previously been shown to contain most informative peaks of exosomes
[2]. A comparison of the mass spectra showed distinct differences particularly in the
range above m/z 8000 after exosome isolation. PLS-DA of the whole dataset
recorded on the MALDI-nanochip showed a good clustering and separation of the
samples belonging to the four study groups (Fig. 2).
Next, tryptic digests of the individual samples were subjected to peptide mass
fingerprinting (PMF) in order to identify the discriminatory peptide peaks between
the study groups based on multivariate data analysis (Fig. 3) and MS/MS analysis
MALDI-nanochip based Screening of Exosomal Biomarkers: Application to Cancer DiagnosticsMichael D. Nairn1; Michael Wuczkowski2; Jesús Jiménez3; Iris Prinz4; Marco Rissoglio5; Emanuele Barborini5,6; Gerald Stübiger2, 7
1Shimadzu, Manchester, United Kingdom; 2Medical University of Vienna, Vienna, Austria; 3Clover Bioanalytical Software, Granda, Spain; 4Stratec Consumables, Salzburg, Austria; 5Tethis, Milan, Italy; 6Luxembourg Institute of Science and Technology; 7Comprehensive Cancer
Center, Vienna, Austria
WP-031
Figure 1 – Overview of the sample preparation and analysis workflow.
Centrifuge blood to isolate the
plasma from the blood sample
Spin at 300g to collect cells
Spin at 10,000g
and discard cell
debris
Spin at 100,000g to collect
Extracellular vesicles
Disclaimer: FOR RESEARCH USE ONLY. NOT FOR USE IN DIAGNOSTIC PROCEDURES.
Collect blood sample from patient
- Apply protein extract
- Perform on-chip tryptic digest
- wash with H2O
Add matrix and acquire MS. Use eMSTAT to identify differentiation peaks. To determine identity of peaks, perform MSMS and submit results to Mascot
Re-suspend the vesicles and extract
proteins
Sequential (ultra)centrifugation MALDI-nanochip processing
MALDI-MS/MS analysisBlood collection and plasma preparation
Figure 2 – Representative protein MALDI mass spectra of selected patient samples of
the four study groups (A-D) recorded (i) before and (ii) after exosome isolation. PLS-DA
plots of all patients of the study groups recorded using (iii) standard target slide and (iv)
after MALDI-nanochip sample processing.
Dataset #1
(+)Proteins_U10000_Patients #1-40 (mz 2100-20000).txt
Group A Group B Group C Group D
Dataset #6 normalized
(+)Proteins_UZ3_Patients #1-40_wash pH9 Tethis (mz 2100-20000).txt
With Blank Removing:
Group A Group B Group C Group D
0
50
100
%Int.
4000 6000 8000
m/z
302 mV 304 mV 130 mV 291 mV
Shimadzu Biotech Axima Performance 2.9.3.20110624
10000 15000 20000
m/z
4.4 mV 16 mV 1.3 mV 19 mV
0
50
100
0
50
100
0
50
100
#5A
#11B
#28C
#38D
4000 6000 8000
m/z
96 mV 7.9 mV 200 mV 190 mV
Shimadzu Biotech Axima Performance 2.9.3.20110624
6179.8 8754.4
10000 15000 20000
m/z
2.4 mV 0.3 mV 4.0 mV 2.0 mV
0
50
100
%Int.
0
50
100
0
50
100
0
50
100
#5A
#11B
#28C
#38D
(i)
(ii)
(iii)
(iv)
CRC preCRC post
IBD
Controls
Figure 3 – Comparison of MS spectra illustrating several peaks where the signal
intensity has been enhanced when using Tethis slide sample washing (Red) versus
Zip-Tip™ sample cleanup (Blue).
was then performed on the discriminant peaks to identify the digested proteins
using Mascot. From in-solution digests peaks of more abundant plasma proteins
(e.g. alpha-1-antitrypsin, immunoglobulin heavy constant alpha 1, haptoglobin,
fibrinogen gamma chain, etc.) were identified in selected samples of the study
groups. These proteins were also found in the MALDI-nanochip processed samples
but they showed no differentiation between the study groups. In contrast, several
Figure 4 – Using a combination of statistical software (eMSTAT) and high
resolution MSMS from HE-CID we are able to discover differences in protein
expression in different patient populations. Differentially expressed proteins were
then targeted for MSMS identification. This precursor was identified as IGHA1 -
Immunoglobulin heavy constant alpha 1.
1578.7
S11 ZT LP50 PE2k MS_0001: J21 (Manual) S11 MTeth LP52 PE2k_0001: 4B1 (Manual)
%In
ten
sit y
0
10
20
30
40
50
60
70
80
90
100
m/z1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300
Processed data (averaged) 69.4 mV 180.4 mV
1641.9
1836.0
1905.01683.0 2090.11550.81884.1
1820.02265.11671.9 1740.9 2045.11946.1 2102.2 2202.21488.8 2157.11888.01612.9 1994.11794.0 2283.1
1706.0 1953.1 2058.01561.81381.7 1471.8 2207.11337.8 2155.11431.8
1641.9
1835.9
1946.01808.01677.8
1874.0
1683.0 1743.9
1856.01794.0 1905.9
1872.01672.91629.81366.8 1740.91550.8 2057.91986.01882.0 2131.01669.71478.8 1816.91604.8 2005.0 2147.01386.7 2202.11951.0 2257.01535.7 2050.01470.81338.7
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